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Two statistical language models have been investigated on their effectiveness in upgrading the accuracy of a Chinese character recognizer. The baseline model is one of lexical analytic nature which segments a sequence of character images according to the maximum matching of words with consideration of word binding forces. A model of bigram statistics of word-classes is then investigated and compared against the baseline model in terms of recognition rate improvement on the image recognizer. On the average, the baseline language model improves the recognition rate by about 7% while the bigram statistics model upgrades it by about 10%
Published in:
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
(Volume:29
,
Issue:
2
)
Date of Publication: Apr 1999
- Page(s):
- 286 - 291
- ISSN :
- 1083-4419
- INSPEC Accession Number:
- 6211039
- Digital Object Identifier :
- 10.1109/3477.752802
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Apr 1999
- Sponsored by :
- IEEE Systems, Man, and Cybernetics Society
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